In this section, we introduce our proposed framework in detail.
Similar to [1]}, [2]}, our Verdi framework for quality estimation is based on the predictor-estimator architecture, where a predictor module is first trained on a parallel corpus and is responsible for extracting features, and an estimator module is trained on QE datasets to evaluate the translation quality by taking the extracted features as input.
Fig.REF  illustrates the overall architecture of our framework which mainly consists of three components: the NMT predictor, the XLM predictor, and the QE estimator.
